Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials.

Autor: Sabanés Zariquiey F; Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain.; Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain., Galvelis R; Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain.; Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain., Gallicchio E; Department of Chemistry, Graduate Center, Brooklyn College, City University of New York, New York, New York 11210, United States., Chodera JD; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States., Markland TE; Department of Chemistry, Stanford University, 337 Campus Drive, Stanford, California 94305, United States., De Fabritiis G; Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain.; Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain.; Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain.
Jazyk: angličtina
Zdroj: Journal of chemical information and modeling [J Chem Inf Model] 2024 Mar 11; Vol. 64 (5), pp. 1481-1485. Date of Electronic Publication: 2024 Feb 20.
DOI: 10.1021/acs.jcim.3c02031
Abstrakt: This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies with the Alchemical Transfer Method and validate its performance against established benchmarks and find significant enhancements compared with conventional MM force fields like GAFF2.
Databáze: MEDLINE
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